Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal JOMLAI: Journal of Machine Learning and Artificial Intelligence
Juwita Juwita
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : JOMLAI: Journal of Machine Learning and Artificial Intelligence

 1 
Naïve Bayes Algorithm For Predicting Sales at the Pematang Siantar VJCakes Store Juwita Juwita; M. Safii; Bahrudi Efendi Damanik
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (781.568 KB) | DOI: 10.55123/jomlai.v1i4.1674

Abstract

Along with the development of the era, competition in the world of business and technology is overgrowing, so business people are competing to develop their business by utilizing existing technology to develop their business, and also so that their business always survives in the rapid business competition. Sales of cake products are expected to continue to increase profits, one of which is by providing products according to market demand so that there are no losses. So far, companies often experience losses because they do not have a system that can predict sales. This writing is done to implement and prove that the Naïve Bayes Algorithm can be used to predict sales of cakes at the VJCakes Pematangsiantar store. The research data is cake sales data consisting of 10 types of cakes with various sizes, tastes, and shapes, which were obtained from the VJCakes Pematangsiantar Store from June 2021 – March 2022. The results of the calculations that have been carried out show that the calculation process is manual and assisted with Rapid software. Miner is the same, which means that the calculation can be said to be successful by producing a probability table of each variable and an accuracy rate of 83.44% of the testing data that has been carried out, and knowing this can be informed to VJCakes to make better decisions in the future.
Co-Authors Bahrudi Efendi Damanik M. Safii